A comparative approach of machine learning models to predict attrition in a diabetes management program. by Samantha Kanny, Grisha Post, Patricia Carbajales-Dale, William Cummings, Janet Evatt, Windsor Westbrook Sherrill

“…These findings underscore the difficulty for models to accurately predict health behavior outcomes, highlighting the need for future research to improve predictive modeling to better support patient engagement and retention.…”
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Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves by Carlos Augusto Alves Cardoso Silva, Rodnei Rizzo, Marcelo Andrade da Silva, Matheus Luís Caron, Peterson Ricardo Fiorio

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Predictive Models for Single-Droplet Ignition in Static High-Temperature Air in Different Gravity Environments by Xiaoyang Lan, Huilong Zheng, Yu Fang, Xianzhang Peng, Xiaofang Yang, Xiaowu Zhang

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Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic by Hayley B Gershengorn, Monisha C Bhatia, Prem Rajendra Warde, Samira S Patel, Tanira D Ferreira, Dipen J Parekh, Kymberlee J Manni, Bhavarth S Shukla

“…Objectives We describe a hospital’s implementation of predictive models to optimise emergency response to the COVID-19 pandemic.Methods We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. …”
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Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models by Osei K. Tweneboah, Kwesi A. Ohene-Obeng, Maria C. Mariani

“…The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s <i>t</i> errors, SV with leverage effects, and a hybrid model combining Student’s <i>t</i> errors with leverage. …”
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Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…We propose a comprehensive and practical framework for Chinese corporate fraud prediction which incorporates classifiers, class imbalance, population drift, segmented models, and model evaluation using machine learning algorithms. …”
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Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder by Praveen Kumar, Priyanka Priyanka, K. V. Uday, Varun Dutt

“…Machine Learning (ML) models can help in the prediction of landslide hazards. …”
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Prediction Models for Risk of Cardiorespiratory Morbidity/Mortality and Fracture Among Young Adults With Cerebral Palsy by Daniel G. Whitney, Edward A. Hurvitz

“…ABSTRACT Background There is a dearth of screening tools for cardiorespiratory disease and fracture risk, such as risk prediction models, for adults with cerebral palsy (CP). …”
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Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures by Yiqun Chen, Mingxuan Ma, Dandan Qu, Chunxiang Xu

“…XGBoost and Transformer models have better performance (AUC 0.866 VS 0.946, F1 0.807 VS 0.889), and both models have better substantial prediction ability for the occurrence of postoperative pneumonia. …”
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Enhancing Engineering and STEM Education With Vision and Multimodal Large Language Models to Predict Student Attention by Luis Marquez-Carpintero, Diego Viejo, Miguel Cazorla

Subjects: “…Attention prediction…”
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Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis by Mong Yung Fung, Yuen Lung Wong, Ka Man Cheung, King Hei Kelvin Bao, Winnie Wing Yan Sung

“…Prediction model study Risk Of Bias Assessment Tool (PROBAST) was adopted for risk of bias assessment. …”
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Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction by Mbah O. M., Madueke C. I., Umunakwe R., Okafor C. O.

“…The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). …”
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From Pixels to Prognosis: Artificial Intelligence and Machine Learning Models in Brain Tumour Mutation Prediction by Quratulain Tariq, Eisha Abid Ali, Saad bin Anis, Irfan Yousaf, Ahmer Nasir Baig, Muhammad Shahzad Shamim

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AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models by Faruk Dikmen, Ahmet Demir, Bestami Özkaya, Muhammad Owais Raza, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai

“…Abstract The integration of artificial intelligence (AI) in wastewater treatment management offers a promising approach to optimizing effluent quality predictions and enhancing operational efficiency. This study evaluates the performance of machine learning models in predicting key wastewater effluent parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen and Total Effluent Phosphorus. …”
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Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack. by Prashant Kumar, Chitra Kushwaha, Dimple Sethi, Debjani Ghosh, Punit Gupta, Ankit Vidyarthi

“…In this paper basically conversed about some deep learning models that will hand over a descent accuracy in prediction of DDoS attacks. …”
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Evaluation of some regression models in the prediction of dissolved oxygen and water temperature of the Jarreh Dam, Iran by Najmeh Anbarpour, Amir Naserin

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DNA methylation patterns and predictive models for metabolic disease risk in offspring of gestational diabetes mellitus by Na Wang, Suping Li, Li Yang

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Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning by Elpri Eka Permadi, Reiko Watanabe, Kenji Mizuguchi

“…Notably, the multitask models with data imputation demonstrated significant improvement in CYP inhibition prediction over the single-task models. …”
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Fine-Tuning Pre-Trained Large Language Models for Price Prediction on Network Freight Platforms by Pengfei Lu, Ping Zhang, Jun Wu, Xia Wu, Yunsheng Mao, Tao Liu

“…This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. …”
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The urban heat Island effect: A review on predictive approaches using artificial intelligence models by Ali Najah Ahmed, Nouar AlDahoul, Nurhanani A. Aziz, Y.F. Huang, Mohsen Sherif, Ahmed El-Shafie

“…While conventional ML algorithms remain widely used, DL and hybrid models have shown superior performance in predictive accuracy. …”
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